Real-time digital twin of autonomous ships based on virtual-physical mapping model

Abstract

The advancement of intelligent technology has propelled the development of smart unmanned vessels into a new phase. To address the urgent demands of current smart ship development, this paper develops a comprehensive ship digital twin system based on a virtual-real mapping algorithm, focusing on the fundamental elements of digital twin model construction. Using the smart unmanned experimental ship Dolphin 1 as a prototype, a digital twin virtual model is proposed. This system leverages real-time internal and external data from the entire vessel to track its navigational status, performance indicators, sailing trends, and surrounding flow field information, offering coordinated “human-machine” navigation assistance. Based on historical data collected from the vessel's long-term navigation, a real-time precise prediction of the vessel's navigational state and hydrodynamic performance is conducted using physics-informed neural network algorithm. This establishes a self-learning iterative virtual-physical mapping model that enables autonomous updates and evolution. As the real navigation data of the vessel continuously update, the virtual model can more accurately simulate the vessel's state in real time. The proposed digital twin model has been tested through sea trials under real sea conditions, demonstrating its high accuracy, robustness, and potential for enhancing navigational safety and efficiency. This system marks a significant step forward in the integration of digital twin technology with maritime navigation, providing a valuable tool for the future development of smart shipping.